lovelyscientist / rna-seq-vae
Variational Auto-Encoder that generates synthetic gene expression data
☆23Updated last year
Related projects ⓘ
Alternatives and complementary repositories for rna-seq-vae
- Biological Network Integration using Convolutions☆59Updated 10 months ago
- A Python toolkit for setting up benchmarking dataset using biomedical networks☆21Updated last week
- Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.☆104Updated 3 months ago
- Interpretation by Deep Generative Masking for Biological Sequences☆36Updated 2 years ago
- High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations☆47Updated last year
- ☆19Updated last year
- accurate prediction of promoter activity and variant effects from massive parallel reporter assays☆29Updated last month
- Template repository for creating novel models with scvi-tools☆19Updated last year
- Batch-adversarial variational auto-encoder (BAVARIA) for simultaneous dimensionality reduction and integration of single-cell ATAC-seq da…☆12Updated last year
- Knowledge-primed neural networks☆34Updated last year
- ExplaiNN: interpretable and transparent neural networks for genomics☆43Updated last year
- Elucidating the Utility of Genomic Elements with Neural Nets☆65Updated last week
- Computational Optimization of DNA Activity (CODA)☆41Updated 2 months ago
- ☆67Updated 3 months ago
- Adversarial generation of gene expression data using Generative Adversarial Networks☆25Updated 3 years ago
- ☆10Updated 2 weeks ago
- CellBox: Interpretable Machine Learning for Perturbation Biology☆54Updated last year
- repository containing analysis scripts and auxiliary files☆30Updated 4 years ago
- Create cell sentences from sequencing data☆21Updated 3 months ago
- Large language modeling applied to T-cell receptor (TCR) sequences.☆47Updated 2 years ago
- ☆56Updated last year
- The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell leve…☆88Updated 3 months ago
- Models and datasets for perturbational single-cell omics☆143Updated 2 years ago
- ☆13Updated 2 years ago
- Stochastic Sequence Propagation - A Keras Model for optimizing DNA, RNA and protein sequences based on a predictor☆42Updated 3 months ago
- Sequential Optimal Experimental Design of Perturbation Screens Guided by Multimodal Priors☆33Updated 6 months ago
- A generative topic model that facilitates integrative analysis of large-scale single-cell RNA sequencing data.☆48Updated 2 years ago
- Code for evaluating single cell foundation models scBERT and scGPT☆30Updated 3 months ago
- Pipeline for efficient genomic data processing.☆20Updated 2 months ago
- ☆17Updated 2 weeks ago